Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H<inf>3</inf>-receptor (H<inf>3</inf>R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H<inf>3</inf>R/D<inf>1</inf>R/D<inf>2</inf>R/5-HT<inf>2a</inf>R) models and by in vitro assays for serotonin 5-HT<inf>1a</inf> and 5-HT<inf>2a</inf> receptor binding of the most promising ligand (71/MBA-VEG8).
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|Titolo:||Predicting targets of compounds against neurological diseases using cheminformatic methodology|
|Appare nelle tipologie:||1.1 Articolo in rivista|
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